Forward Deployed Engineer

Dialpad

About The Position

The Forward Deployed Engineer (FDE) is a customer-embedded delivery engineer responsible for turning qualified AI opportunities into live, production-ready agent deployments. This role sits at the intersection of customer workflow design, applied AI engineering, enterprise integration, and production rollout. The core mission is to close the last-mile gap between platform capability and measurable customer outcomes by taking shaped opportunities from scoping through implementation and into the first production shift. You will work directly with customer stakeholders, internal delivery teams, and product partners to design, build, evaluate, deploy, and stabilize AI agents in real operating environments. This is a highly customer-facing, hands-on role for someone who can translate ambiguity into working systems and can move fluidly between technical depth and business impact.

Requirements

Experience in a high-impact, customer-facing technical role such as Forward Deployed Engineering, Solutions Architecture, Technical Consulting, Deployment Engineering, or AI-focused SaaS implementation.
Hands-on experience building and deploying AI, LLM, or agentic systems in production environments.
Strong working knowledge of modern AI and application patterns, including LLMs, prompt engineering, retrieval systems, vector databases, evaluation methods, and agent orchestration frameworks.
Proficiency in Python, JavaScript/TypeScript, Go, or similar languages used for integrations, middleware, and production deployment work.
Experience working with APIs, enterprise integrations, and workflow automation across customer environments.
Ability to translate ambiguous business objectives and SOPs into clear, shippable technical solutions.
Keen attention to detail allowing you to quickly analyze results, showcase value-add to a customer, and iterate quickly.
Strong customer communication skills, including the ability to explain trade-offs, risks, timelines, and technical concepts to both technical and non-technical stakeholders.
High ownership, strong judgment under ambiguity, and a bias toward action and measurable outcomes.

Responsibilities

Convert prioritized use cases and pre-sales assumptions into a clear delivery scope, technical plan, and phased implementation approach.
Work directly with customer technical and operational teams to map real workflows, exception paths, approvals, escalation rules, and human-in-the-loop controls.
Design and build customer-specific AI agent solutions, including orchestration logic, prompt architecture, retrieval flows, workflow rules, and decision logic.
Implement integrations with customer data sources, APIs, SaaS platforms, CRMs, ERPs, and enterprise knowledge systems so agents can perform real work end to end.
Develop evaluation methods, guardrails, and acceptance criteria that measure agent usefulness, reliability, safety, task success, and production readiness.
Run iterative testing, failure analysis, and performance tuning to improve quality, groundedness, latency, cost, and operational effectiveness.
Prepare solutions for production by addressing access controls, observability, rollout sequencing, exception handling, fallback behavior, and support readiness.
Lead deployment activities through initial go-live and first-shift stabilization, including rapid issue resolution, workflow adjustments, and customer-facing hypercare.
Document reusable implementation patterns, operational runbooks, known failure modes, and recommended next improvements to support scale and repeatability.
Feed field learnings back into Product, Engineering, and transformation teams so future deployments become faster, safer, and more repeatable.
Detailed solution scoping after deal alignment.
Customer-specific implementation and agent behavior design.
Technical integration across enterprise systems and data sources.
Evaluation, iterative quality improvement, and production hardening.
Go-live readiness and first-shift production stabilization.
Executive value framing before opportunity qualification.
Broad transformation-roadmap design across multiple business units.
Portfolio-level governance program management.
Long-term managed-service ownership after the initial deployment phase, unless the service model explicitly extends the role.